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  Unsupervised community detection in attributed networks based on mutual information maximization

Zhu, J., Li, X., Gao, C., Wang, Z., Kurths, J. (2021): Unsupervised community detection in attributed networks based on mutual information maximization. - New Journal of Physics, 23, 113016.
https://doi.org/10.1088/1367-2630/ac2fbd

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 ???ViewItemFull_lblCreators???:
Zhu, Junyou1, ???ENUM_CREATORROLE_AUTHOR???
Li, Xianghua1, ???ENUM_CREATORROLE_AUTHOR???
Gao, Chao1, ???ENUM_CREATORROLE_AUTHOR???
Wang, Zhen1, ???ENUM_CREATORROLE_AUTHOR???
Kurths, Jürgen2, ???ENUM_CREATORROLE_AUTHOR???           
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1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 ???ViewItemFull_lblAbstract???: Community detection is of great significance for understanding network functions and behaviors. With the successful application of deep learning in network-based analyses, recent studies have turned to utilizing graph convolutional networks (GCNs) to this problem due to their capability in capturing network attributes. Nevertheless, most existing GCN-based community detection approaches are semi-supervised and local structure-aware, even though community detection is an unsupervised learning problem essentially. In this paper, we develop a novel GCN method for unsupervised community detection under the framework of mutual information (MI) maximization, called UCDMI. Specifically, a novel MI maximization mechanism is developed to capture more fine-grained information of the global network structure in an unsupervised manner. Moreover, a new aggregation function is proposed for GCN to distinguish the importance between different neighboring nodes, which enables our method to identify more high-quality node representations and improve the community detection performance. Our extensive experiments demonstrate the effectiveness of our proposed UCDMI compared with several state-of-the-art community detection methods.

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 ???ViewItemFull_lblDates???: 2021-11-092021-11-09
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 ???ViewItemFull_lblIdentifiers???: ???ENUM_IDENTIFIERTYPE_DOI???: 10.1088/1367-2630/ac2fbd
???ENUM_IDENTIFIERTYPE_PIKDOMAIN???: RD4 - Complexity Science
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???ENUM_IDENTIFIERTYPE_OATYPE???: Gold Open Access
???ENUM_IDENTIFIERTYPE_RESEARCHTK???: Complex Networks
???ENUM_IDENTIFIERTYPE_RESEARCHTK???: Nonlinear Dynamics
???ENUM_IDENTIFIERTYPE_MODELMETHOD???: Nonlinear Data Analysis
???ENUM_IDENTIFIERTYPE_MODELMETHOD???: Machine Learning
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???ViewItemFull_lblSourceTitle???: New Journal of Physics
???ViewItemFull_lblSourceGenre???: ???ENUM_GENRE_JOURNAL???, SCI, Scopus, p3, oa
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???ENUM_IDENTIFIERTYPE_PUBLISHER???: IOP Publishing